System for automatic fall detection for elderly people

ABSTRACT

Apparatus for detection of human falls comprises: an acceleration detector, for detecting vibration events, typically placed on a floor, a microphone, located in association with the acceleration detector for detection of corresponding sound events, and a classification unit to classify concurrent events from the microphone and the acceleration detector, thereby to determine whether a human fall is indicated. If the event appears to be a human fall, then an alarm is raised.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/453,012 filed on Apr. 23, 2012, which is a continuation of U.S.patent application Ser. No. 12/320,521 filed on Jan. 28, 2009, now U.S.Pat. No. 8,179,268, which claims the benefit of priority of U.S.Provisional Patent Application No. 61/064,508 filed on Mar. 10, 2008.The contents of all of the above applications are incorporated byreference as if fully set forth herein.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to a device and method for automatic falldetection for elderly people.

Falls are a major risk for the elderly people living independently. Thestatistics of falls show that approximately one in every three adults 65years old or older falls each year, and 30% of those falls result inserious injuries. Rapid detection of fall events can reduce the rate ofmortality and raise the chances to survive the event and return toindependent living. In the last two decades several technologicalsolutions for detection of falls were published, but most of them sufferfrom critical limitations.

The aging of baby boomers has become a social and economic challenge.Due to the maturation of the baby boomers generation, the United Nationpredicts that by the year 2035, 25% of the world population will be aged65 years or older. In the year 2000 this group accounted for 10% ofpopulation compared to 6.9% in 1900. In the United States alone, thenumber of people over the age 65 is expected to hit 70 million by 2030,doubling from 35 million in 2000, and similar increases are expectedworldwide. This demographic trend is already posing many social andeconomic problems. With the aging population comes a necessity todevelop more efficient and cost-effective methods of health monitoringand support for elderly people.

Falls and sustained injuries among the elderly are a major problemworldwide, and are the third cause of chronic disability according tothe World Health Organization. The proportion of people sustaining atleast one fall during one year varies from 28-35% for the age of 65 andover, while falls often signal the “beginning of the end” of an olderperson's life. The risk of falling increases with age, and in 2 casesout of 3 it happens at home. People that experience a fall event athome, and remain on the ground for an hour or more, usually die within 6months.

In the past two decades there have been many commercial solutions andacademic developments aimed at automatic and non automatic detection offalls.

-   -   A. Social Alarm: The social alarm is a wrist watch with a button        that is activated by the subject in case of a fall event. The        main problem with that solution is that the button is often        unreachable after the fall especially when the person is        panicked, confused, or unconscious.    -   B. Automatic Fall Detector: The most popular solutions for        automatic detection of falls are the wearable fall detectors        that are based on combinations of accelerometers and tilt        sensors, for example devices based on a combination of shock and        tilt sensors. An alternative uses three accelerometers to obtain        the position, speed and acceleration vector of the person. Noury        et al. developed a device that is placed under the armpit, and        employs two accelerometers and a microcontroller to compute the        orientation of the body. A critical disadvantage of those        solutions is that the person has to wear the device in the        shower, a place with a high occurrence rate of falling, which        means both that the device has to be waterproof, and furthermore        people prefer not to wear anything while showering. Moreover,        these devices produce many false alarms, and old people tend to        forget wearing them frequently.    -   C. Video Analysis-Based Fall Detection System: There are a few        solutions from recent years that are based on image processing        of the person's movement in real-time. One work analyzes the        vertical and horizontal speeds during a fall. Another develops a        networked video camera system that detects moving objects,        extracting features such as object speed and determines if a        human fall has occurred.

Camera based solutions suffer from particular disadvantages such asprivacy concerns, (critical to encouraging takeup), and difficulty ineffectively monitoring the entire area of a house where falls may takeplace.

Due to the disadvantages of the existing fall detection techniques,there is a need for a better solution for the elderly fall detection.The idea of floor vibrations was suggested by Alwan et al. M. Alwan, P.Rajendran, S. Kell et al., “A smart and passive floor-vibration basedfall detector for elderly,” in Proceedings ICTTA '06, Damascus, Syria,Apr. 2006, pp. 23-28.

SUMMARY OF THE INVENTION

We have developed a solution that is based on the combination of floorvibrations detection and sound during a fall, and does not require thesubject to wear anything however, The present embodiments further differover Alwan et al, in use of different sensors, features and patternrecognition algorithms that are implemented in the system. According toone aspect of the present invention there is provided apparatus fordetection of human falls, comprising:

an acceleration detector, for detecting vibration events,

a microphone, for placing in association with said acceleration detectorfor detection of sound events, and

a classification unit configured to classify concurrent events from saidmicrophone and said acceleration detector, thereby to determine whethera human fall is indicated.

There may additionally be an alarm unit, associated with saidclassification unit, for providing an alarm output when said human fallis indicated.

In an embodiment, said acceleration detector is attached to a floor.

In an embodiment, said concurrent events comprise vibration events, andsound events.

In an embodiment, said classification unit is configured to extract fromsaid events a shock response spectrum (SRS) feature.

In an embodiment, said classification unit is configured to extract fromsaid events a Mel frequency cepstral coefficient (MFCC).

In an embodiment, said classification unit is configured to extract fromsaid sound events a sound event length feature and a sound event energyfeature.

In an embodiment, said classification unit is configured to extract fromsaid vibration events a vibration event length and a vibration eventenergy.

In an embodiment, said classification unit is configured to extractfeatures from said events, and to compare a distribution of saidfeatures with prestored features of learned events, thereby to classifysaid events as belonging to a human fall or not belonging to a humanfall.

In an embodiment, said extracted features comprise shock responsespectra, Mel frequency cepstral coefficients, vibration event energy,vibration event length, sound event energy and sound event length.

In an embodiment, said extracted features comprise seventeen features.

According to a second aspect of the present invention there is provideda method for detection of human falls, comprising:

detecting vibration events,

detecting sound events,

classifying concurrent vibration and sound events to determine whether ahuman fall is indicated, and

providing an alarm output when said human fall is indicated.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. The materials, methods, andexamples provided herein are illustrative only and not intended to belimiting.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. This refers in particular totasks involving the control of the spectral equipment.

Moreover, according to actual instrumentation and equipment ofembodiments of the method and/or system of the invention, severalselected tasks could be implemented by hardware, by software or byfirmware or by a combination thereof using an operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of thepreferred embodiments of the present invention only, and are presentedin order to provide what is believed to be the most useful and readilyunderstood description of the principles and conceptual aspects of theinvention. In this regard, no attempt is made to show structural detailsof the invention in more detail than is necessary for a fundamentalunderstanding of the invention, the description taken with the drawingsmaking apparent to those skilled in the art how the several forms of theinvention may be embodied in practice.

In the drawings:

FIG. 1 is a simplified diagram illustrating a first fall detectiondevice according to an embodiment of the present invention;

FIG. 2 is a simplified diagram showing training and testing phases inthe device of FIG. 1;

FIG. 3 shows a detail of event detection in the flow chart of FIG. 2;

FIG. 4 shows a detail of segmentation in the flow chart of FIG. 2;

FIGS. 5 a and 5 b are simplified plots showing segmentation andbackground noise, applied to detection by the device of FIG. 1;

FIG. 6 a shows a vibration event within a continuous signal, FIG. 6 bshows a sound event within a continuous signal, FIG. 6 c shows avibration event after extraction, and FIG. 6 d shows a sound event afterextraction;

FIG. 7 is a simplified diagram showing a human as an shock responsesystem as modeled by an embodiment of the present invention;

FIG. 8 is a simplified diagram showing the Shock response system modelused for calculations, according to an embodiment of the presentinvention;

FIG. 9 is a graph showing acceleration against SDOF system frequency fora particular test event using the present embodiment;

FIG. 10 is a graph of an SRS feature plotted against length of vibrationevent and showing a line that divides the graph into human fall andother event;

FIG. 11 is a graph of a vibration energy feature plotted against lengthof sound event and again showing a line that divides the graph intohuman fall and other event;

FIG. 12 is a flow chart showing event detection and segmentationalgorithms used in the testing phase of the present embodiments;

FIGS. 13 a and 13 b are sequential shots in a fall test of a humanmimic; and

FIG. 14 is a graph of percentage success rate for sensitivity andspecificity as shown by the results of the presently described tests.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present embodiments provide an automatic fall detection system forelderly people. The system is based on floor vibration and acousticsensing using two or more sensors, and uses signal processing andpattern recognition algorithm to discriminate between human fall eventsand other events, such as inanimate object falls. The proposed solutionis inexpensive, and does not require the person to wear anything. It maydetect fall events in critical cases in which the person is unconsciousor in a stress condition. Results obtained with an embodiment providedfor detection of human falls with a sensitivity of 97.5% and specificityof 98.5%.

A human fall on the floor creates a shock signal that propagates throughthe floor. Any type of floor is suitable, although different floors havedifferent properties in terms of signal transmission. In a daily routinethere are a lot of sounds in the house, but, as will be describedhereinafter, when there is a particular combination of a fall-specificvibration event with a corresponding sound event, there is a suspicionof a human fall on the floor. The present embodiments make use of acombination of vibration and sound sensors because they can supplyinformation about the way the fall vibrates the floor and how it sounds.The combination is explained hereinbelow. The proposed fall detectionsystem comprises a passive solution that does not require the person towear anything. The system is based on the detection of vibration andsound signals from an accelerometer and a microphone. The mainhypothesis is that in most cases we can accurately identify human fallsand discriminate them from other events using sound and vibrationdetection in conjunction with advanced signal processing techniques. Thepresent embodiments aim to provide a complete solution, which consistsof an automatic algorithm that is based on signal processing and patternrecognition techniques, and is able to demonstrate detection of falls.Algorithms described herein enable a distinction to be made between ahuman fall event and other events such as the fall of an object on thefloor, or the vibrations and sounds of mere daily activities. In theexperiments herein described, an accelerometer and microphone werelocated at the side of a room, close to the wall, and connected to thefloor by scotch tape. The accelerometer was attached to the floor, themicrophone was above the accelerometer, and the scotch tape connectedthem both to the floor.

The principles and operation of an apparatus and method according to thepresent invention may be better understood with reference to thedrawings and accompanying description.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not limited in its applicationto the details of construction and the arrangement of the components setforth in the following description or illustrated in the drawings. Theinvention is capable of other embodiments or of being practiced orcarried out in various ways. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting.

Reference is now made to FIG. 1 which is a generalized block diagramshowing a simplified conceptual view of the fall detection systemaccording to a first embodiment of the present invention. Apparatus 10for detection of human falls, comprises an acceleration detector 12 fordetecting vibration events. The acceleration detector is typically anaccelerometer. Alternatively, any other vibration detector may be used.A microphone 14 is located in association with the acceleration detector12 and serves for detection of sound events. Other detectors may also beused, for example humidity detectors, temperature detectors, lightingdetector etc as may seem useful. The acceleration detector may be asingle axis acceleration detector or multi-axis acceleration detector. Aclassification unit 16, typically provided within an electronicprocessor, classifies concurrent events detected by the microphone andthe acceleration detector. The processor uses data of the event asprovided by the accelerometer and the microphone, to make a decision asto whether or not a human fall is indicated. An alarm unit 18 uses theoutput decision of the classification unit and provides an alarm outputwhen the human fall is indicated. The output may be provided to arelative, a care supervisor or to emergency or medical services, asdeemed appropriate. In one embodiment the microphone and an associatedloudspeaker may subsequently be used to open a voice channel to allowthe output recipient to make voice contact in order to better assess thesituation. Typically, the acceleration detector 12 is attached to afloor, so as best to pick up vibrations caused by the fall.

The concurrent events as detected are formed by combining vibration datafrom the accelerometer and sound data from the microphone, into a singlecombined event that is assessed together.

The data received from the accelerometer and from the microphone is inthe form of analogue waves. The waves may initially be digitized by A/Dconverter 20. Subsequently features are extracted from the waveforms andit is the extracted features which are assessed and compared. Numerousfeatures can be derived from waveforms. As will be discussed below, alimited number of features was chosen experimentally and found incombination to define human falls with high reliability. However anyother features or combination of features which is found to providereliable identification of human falls, is contemplated by the presentembodiments.

In an embodiment the classification unit is configured to extract fromthe events and associated waveforms a shock response spectrum (SRS)feature. Another feature is a Mel frequency cepstral coefficient (MFCC).From the sound events a sound event length feature and a sound eventenergy feature may be extracted. From the vibration events, a vibrationevent length and a vibration event energy may be extracted.

A distribution of the extracted features is then compared withdistributions in prestored features of learned events. FIGS. 10 and 11,to be described below, show distributions in which a line divides humanfall events from other events. The comparisons allow for classificationof events as belonging to a human fall or not belonging to a human fall.

Up to seventeen different features were used in the present embodiments,although this number is merely exemplary and any other number offeatures may be equally as good, as will be discussed in greater detailbelow. Furthermore the features selected may be replaced by alternativefeatures.

In use, the apparatus of claim 1 detects vibration events, andsimultaneously detects sound events. Concurrent vibration and soundevents are analyzed and features extracted. The feature distribution iscompared to previously learned events to determine whether a human fallis indicated. Finally, an alarm output is provided when a human fall isindicated.

If different detectors are placed in different rooms, then an alarm mayindicate which detector has been triggered, thus indicating in whichroom the fall occurred. Where several detectors cover a room, say in thecase of a large room, the alarm may indicate in which room the fall hasoccurred.

In an embodiment, the sensors that are used include a Crossbow CXL02LF1Zaccelerometer (San-Jose, Calif.) and a small amplified microphone(MS-3100W), both being attached to the floor. The acquired signals aretransmitted to a portable NI USB-6210 (National Instruments, TX, USA)data acquisition device that samples the signals at 16 kHz and transmitsthem to a PC.

As a rule of thumb, if a machine produces high amplitude vibrations(greater than 10 g rms, where g is the gravitational acceleration ofearth) at the measurement point, a relatively low sensitivity (10 mV/g)sensor is preferable. If the vibration is less than 10 g rms, a highsensitivity (100 mV/g) sensor should generally be used. The accelerationof the floor in an event of a human fall is in a scope of 1-2g with lowamplitude vibrations. Therefore, we choose to use an accelerometer witha sensitivity of 1 V/g with the ability to sense accelerations up to 2g.The chosen microphone has a Frequency range of 20-16000Hz, and S/N Ratioof more than 58 dB.

Using an energy-based event detection algorithm, floor vibrations aremonitored and events are detected. Vibration and sound features areextracted from these events and classified based on a patternrecognition algorithm that discriminates human fall from other events.If the algorithm reports a human fall, an alarm is activated.

Fall Detection and Classification Algorithm

A block diagram of the fall detection and classification system is shownin FIG. 2.

The system contains two phases of data analysis: a training phase 30 anda testing phase 32. Both phases use vibration and sound signals asinputs. In order to trigger the classification algorithm, a significantevent in the vibration signal is found. Once an event is detected in thevibration signal, the sound signal is analyzed. The sound waves thatpropagate in the air are slower than the vibration waves that propagatein the floor. Nevertheless, the length of the time delay between thesignals is much shorter than the time length of the events so thatsimultaneity can still be defined, and the analysis of the signal can beperformed as will be shown ahead.

The training phase 30 consists of event detection and segmentation 34,feature extraction 36, feature selection 36 and model estimation 38modules. In the training phase of the algorithm, we estimate twoclassification models for different types of events. In our system thereare two classes of classification: “human fall” 40 and “other event” 42.

Event Detection and Segmentation (Training Phase)

Reference is now made to FIG. 3, which is a simplified flow diagramwhich describes detection and segmentation of a vibration event fromwithin a vibration signal. The purpose of the event detection algorithm,module 34, is to detect a vibration event in the vibration signal v(t)at time index t_(e), and to segment it. The event detection andsegmentation are performed using energy calculations e_(f)(n) fromrunning time-frames:

$\begin{matrix}{{{{e_{f}(n)} = {\sum\limits_{t = {{({n - 1})} \times L_{f}}}^{{n \times L_{f}} - 1}\;{v^{2}(t)}}};}{{n = 1},\ldots\mspace{14mu},{N_{f} - 1}}} & (1)\end{matrix}$

where e_(f)(n) represents the energy of the n^(th) time frame, N_(f) isthe total number of frames in the signal v(t), and L_(f) is the lengthof each frame (20 msec. in this case).

Module 34 operates the event detection algorithm on a recorded finitevibration signal in the training phase. In FIG. 3, the input vibrationsignal is received in box 48, is entered into equation 1, in box 50, toobtain an array e_(f)(n). Then in box 52, e_(max) is obtained, being themaximum value of the array e_(f)(n), where n_(e) is the frame index ofthe event, and in box 54, t_(e) is obtained, being the time index of theevent.

Reference is now made to FIG. 4, which illustrates the array analysisfor segmentation. After finding t_(e) by the event detection algorithm,the event segmentation algorithm extracts the event from the recordedfinite vibration signal. Segmentation of the vibration event isperformed by an automatic algorithm that identifies the boundaries ofthe event. The boundaries of the event t_(start) and t_(end) arecalculated by an algorithm that is based on an automatic noise thresholdcalculation.

FIG. 4 describes the event segmentation algorithm on a recorded finitevibration signal in the training phase. In this figure n_(end)represents the frame index of the end of the event, n_(start) is theframe index of the beginning of the event, t_(end) is the time index ofthe end of the event, t_(start) is the time index of the beginning ofthe event and e_(th1) is the threshold of the frame energy.

Calculation of the energy threshold e_(th1): The value of e_(th1) iscalculated by assessing the e_(hist) which is the most prominent valueof e_(f)(n). e_(hist) is calculated from a binned histogram of therecorded finite vibration signal. The value of e_(th1) is calculated bye _(th1) =e _(hist)×(1+c ₁)  (2)

where c₁ is an empirical threshold coefficient (set to 0.2).

In the method the array is analyzed first by ascent to obtain the end ofthe event and then by descent to obtain the beginning of the loop.

FIGS. 5 a and 5 b between them show an example of event segmentation.FIG. 5 a is a figure showing three signal energies, a maximum, an eventbeginning and an event end. and FIG. 5 b shows an example of a histogramcalculation of the array e_(f)(n) with a resolution of 0.00001. As seenin FIG. 5 b, the background noise energy distribution is shown. Usingthe noise distribution the locations of the beginning and end of theevent can be determined. The threshold in this example is calculated tobe 0.0067 (using 20 msec. frame size).

After detection and segmentation of a vibration event, segmentation ofthe sound event from the sound signal is performed. Starting from t_(e),the algorithm finds the boundaries of the sound event using the samesegmentation technique mentioned above.

FIGS. 6 a-d shows an example of the results of the event detection andsegmentation algorithms implemented on a vibration and sound signals ofa fall event. Specifically, FIG. 6 a shows a vibration event within acontinuous signal, FIG. 6 b shows a sound event within a continuoussignal, FIG. 6 c shows a vibration event after extraction, and FIG. 6 dshows a sound event after extraction. It will be appreciated that otheralgorithms for event segmentation are available and may be selected bythe skilled person.

Feature Extraction (Training & Testing Phases)

The classification problem is to distinguish between events of human andobject falls. Following the event detection and segmentation, featuresare extracted from these vibration and sound event signals (trainingphase trials) for model estimation. The selection of the completefeature set, the subset of features to be used from the complete featureset, and the design of the model which uses these selected features isof central importance for obtaining high classification accuracy. Thecomplete set of features that were chosen as candidates for the modelare composed of two kinds of features: temporal features and spectralfeatures.

-   -   1. Temporal Features: The features that are extracted from the        vibration and sound events are length (time) and energy (sum of        square of the amplitudes over time), a total of four temporal        features.    -   2. Spectral Features: Shock response spectrum (SRS) features are        extracted from the vibration event signal, and Mel frequency        cepstral coefficients (MFCC) may be extracted from the sound        event signal.

Table 1 summarizes the overall set of the candidate features.

TABLE 1 Summary of candidate features Kind of No. of Vibration/SoundFeature feature Feature name features feature symbol Temporal Vibrationevent 1 Vibration L1 features length Sound event 1 Sound L2 lengthVibration event 1 Vibration E1 energy Sound event 1 Sound E2 energySpectral SRS 93 Vibration S1-S93 features MFCC 13 Sound C1-C13

Shock Response Spectrum (SRS): For the analysis of the floor's vibrationsignal, we use a physical approach for the description of thehuman-floor system. Robinovitch et al S. N. Robinovitch, W. C. Hayes,and T. A. McMahon, “Distribution of contact force during impact to thehip,” Annals of Biomedical Engineering, vol. 25, no. 3, pp. 499-508,1997, describe the dynamics of impact to the hip during a fall event asa Mass-Spring system as per FIG. 7. A mass spring approach justifies theuse of the shock response spectrum analysis that is popular in manyengineering fields for vibration signal analysis.

The SRS calculation is a transform that assumes that the fall event is amass-spring system. The SRS is the peak acceleration responses of alarge number of single degree of freedom (SDOF) systems each one with adifferent natural frequency (FIG. 8). It is calculated by convolutionintegral of the measured signal (input) with each one of the SDOFsystems.

A typical scheme of the frequencies of the x axis is based on aproportional bandwidth, such as ⅙ octave. This means that eachsuccessive natural frequency is 2^(1/6) times the previous naturalfrequency. FIG. 9 is an example of the SRS plot of one of the vibrationevents as measured by an accelerometer of the present embodiments. TheSRS transform has a total of 133 values, but at very low frequenciesmany of those values are close to zero. Therefore, we choose 93 valuesof the SRS as candidate features from the frequency bandwidth of10.1-2.048 Hz of a specific vibration event.

Mel Frequency Cepstral Coefficients (MFCC): Mel Frequency Cep stralCoefficients (MFCCs) represent audio signals with frequency bands thatare positioned logarithmically (on the Mel scale) and approximate thehuman auditory system's response more closely than the linearly-spacedfrequency bands obtained directly from the FFT of the signal. The use ofthese coefficients is popular in speech and speaker recognition, andfits our goal of characterizing the sound of a human fall and otherevents. In our study there are different kinds of sound signals. Thesignal may vary from short events, such as human steps, to long eventssuch as a human fall. We divide the sound event signals into windowswith length of 0.03 seconds, and calculate the MFCC coefficients foreach window. The MFCC transform supplies 13 features for each window,when the first feature is the energy of the window. By choosing thewindow with the maximum first feature, that is the most ‘energetic’window, we extract 13 MFCC features from each sound event signal.

Feature Selection (Training Phase)

The problem of selecting a subset of features from N-dimensional featuremeasurement vector is called feature selection. The feature selectionprocedure reduces the cost of the pattern recognition process, andprovides better classification accuracy due to finite training datasetsize effects. There are several feature selection procedures discussedin the pattern recognition literature such as: sequential forwardselection (SFS), sequential backward selection (SBS), 1-r algorithm,sequential floating forward sequence (SFFS), and others.

The sequential forward floating selection (SFFS) method has beensuggested to be the most powerful algorithm for feature selection. Onthis basis we use the SFFS algorithm with Mahalanobis distance testcriterion for performance evaluation of the featuresD(Z)=(μ₁−μ₂)^(T) C ⁻¹(μ₁−μ₂)  (3)

Where Z is the set of the feature vectors, C is the covariance matrix ofthe feature vector, and μ₁, μ₂ are the mean vectors of each class(“human fall” and “other event”).

The Mahalanobis distance test criterion is the appropriate one fordistance measurement of features with a Gaussian distribution.

Three major steps are identified in the SFFS algorithm: the first is“Inclusion”, the second is “Test”, and the last is “Exclusion”. SFFSbegins with the inclusion process to select a feature with bestperformance. The searching process is followed by conducting a test onevery feature selected in the same iteration to specify features thatmay degrade the overall performance. If such a feature exists, SFFS maycommence an exclusion process to ignore such a feature. The algorithmmay continue looking for other better features until all features areexamined.

The algorithm for feature selection ranks the performance of 110features, as per table 1 above, and chooses a set of 17 top performingfeatures for event classification, see the results section below.

Model and Classifier Estimation (Training Phase)

In the training phase, we use the features of “human” fall events andfeatures of “other” events to estimate two models: a “human fall” eventmodel and an “other” event model.

Many different algorithms have been developed to classify unknownpattern samples on the basis of a specified set of features. Theclassification algorithms described herein are based on Bayesclassification although other algorithms are available to the skilledperson.

The Bayes decision rule classifies an observation vector z to the classthat has the highest a posteriori probability among the two classes

$\begin{matrix}{{\omega(z)} = {\underset{\omega \in \Omega}{\arg\;\max}\left\{ {P\left( {\omega ❘z} \right)} \right\}}} & (4)\end{matrix}$

Where ω(z) is the chosen class, z is the observation feature vector,P(ω|z) is the a posteriori probability, and Ω={ω₁, ω₂} is the classspace.

The Bayes decision rule in terms of a priori probabilities and theconditional probability densities is

$\begin{matrix}{{\omega(z)} = {\underset{\omega \in \Omega}{\arg\;\max}\left\{ {{p\left( {z❘\omega} \right)}{P(\omega)}} \right\}}} & (5)\end{matrix}$

where p(z|ω) is the conditional probability density function (thelikelihood function of z given ω), and P(ω) is the class a prioriprobability.

In this study, the a priori probabilities of “human fall” events and“other” events are not known. Therefore, the a priori probabilities forthe two classes are assumed to be equal. Hence,

$\begin{matrix}{{{{P\left( \omega_{k} \right)} = \frac{1}{2}};}{{k = 1},2}} & (6)\end{matrix}$

where k is the class index.

The training dataset found to have a Gaussian distribution for eachclass. Therefore Gaussian models were estimated for each class. TheGaussian conditional density function is

$\begin{matrix}{{p\left( {z❘\omega_{k}} \right)} = {\frac{1}{\sqrt{\left( {2\;\pi} \right)^{N}{C_{k}}}}{\exp\left( \frac{{- \left( {z - \mu_{k}} \right)^{T}}{C_{k}^{- 1}\left( {z - \mu_{k}} \right)}}{2} \right)}}} & (7)\end{matrix}$

where C_(k) is the kth class covariance matrix, μ_(k) is the kth classexpectation vector, and N is the feature space dimension.

The kth class expectation vector μ_(k) is estimated by

$\begin{matrix}{\mu_{k} = {\frac{1}{N_{k}}{\sum\limits_{n = 1}^{N_{k}}\; z_{n}}}} & (8)\end{matrix}$

where N_(k) is the number of samples with class ω_(k), and z_(n) is themeasurement features vector.

The kth class covariance matrix C_(k) is estimated by

$\begin{matrix}{C_{k} = {\frac{1}{N_{k} - 1}{\sum\limits_{n = 1}^{N_{k}}\;{\left( {z_{n} - \mu_{k}} \right)\left( {z_{n} - \mu_{k}} \right)^{T}}}}} & (9)\end{matrix}$

The model parameters that are stored for each class are the expectationvector μ_(k) and the covariance matrix C_(k).

Substitution of (6) and (7) in (5) gives the following Bayes decisionrule

$\begin{matrix}{{\omega(z)} = {{\omega_{i}\mspace{11mu}{with}\mspace{14mu} i} = {\underset{{k = 1},2}{\arg\;\max}\left\{ {\frac{1}{\sqrt{\left( {2\;\pi} \right)^{N}{C_{k}}}}{{\exp\left( \frac{{- \left( {z - \mu_{k}} \right)^{T}}{C_{k}^{- 1}\left( {z - \mu_{k}} \right)}}{2} \right)} \cdot \frac{1}{2}}} \right\}}}} & (10)\end{matrix}$

We take the logarithm of the function between braces without changingthe result of the argmax{ } function. Therefore (10) is equivalent to

$\begin{matrix}{{\omega(z)} = {{\omega_{i}\mspace{11mu}{with}\mspace{14mu} i} = {\underset{{k = 1},2}{\arg\;\max}\left\{ {{{- \frac{1}{2}}\ln{C_{k}}} - {{\frac{1}{2} \cdot \left( {z - \mu_{k}} \right)^{T}}{C_{k}^{- 1}\left( {z - \mu_{k}} \right)}}} \right\}}}} & (11)\end{matrix}$

Equation (11) calculates the maximum likelihood, and chooses class ω_(i)(i=1,2) for a specific vector z in the N dimensional features space.That classifier is called quadratic classifier.

Following the estimation of the models we may calculate the boundariesbetween the two models by using the quadratic classifier function (Eqs.(11)), and use the estimated classifier to classify events in thetesting phase of the algorithm. FIGS. 10 and 11 are plots of twoselected features with a quadratic classifier. Specifically FIG. 10shows an SRS feature against length of vibration event. A curved linedifferentiates between human falls and other events. FIG. 11 showsvibration energy plotted against length of the corresponding soundevent. Again a curved line distinguishes between the human fall andother events. The data of those Figures was collected in the trainingphase trials that will be discussed later.

Event Detection and Segmentation (Testing Phase)

In the testing phase the signal is continuous. Therefore, the algorithmanalyzes finite windows of the vibration signal by calculating theenergy of the window (e_(w)(m)), and finds a finite suspected-eventwindow. When there is a suspected-event window, event detection andsegmentation, feature extraction, and event classification algorithmsare performed. Event classification is based on the estimated models ofthe events.

Calculation of an m^(th) event-suspected window energy value e_(w)(m) isperformed by

$\begin{matrix}{{{{e_{w}(m)} = {\sum\limits_{t = {{({m - 1})} \times {Dw}}}^{{{({m - 1})} \times {Dw}} + {Tw}}\;{v^{2}(t)}}};}{{m = 1},2,\ldots}} & (12)\end{matrix}$

where T_(w) is the length of the event-suspected window (20 sec. in thiscase), D_(w) is the time difference between event-suspected windows (10sec. in this case), and v(t) is the vibration signal.

FIG. 12 is a simplified diagram illustrating the event detectionalgorithm in the testing phase. In this figure, c₂ is an empiricalthreshold coefficient (set to 0.1), and e_(th2) is the time-averagedadaptive noise level.

The value of e_(th2) is recalculated every D_(w) seconds by the sametechnique as e_(th1) is calculated, say by assessing the most prominentvalue of e_(w)(k) which is calculated from a binned histogram of thepreceding 10 minutes.

Event Classification (Testing Phase)

Following the segmentation of the vibration and sound event signals fromthe finite event-suspected window, 17 selected features are extractedfrom the testing data signals, in this case 13 features from vibrationsignals, and 4 features from sound signals.

The values of the extracted features are substituted in the17-dimentional estimated classifier (Eqs. (11)). Following thecalculation of the maximum likelihood, the classifier returns aclassification result as to whether the event is a ‘human fall’(Positive) or it is ‘other event’ (Negative).

Experimental Setup

The training and testing data sets for the algorithm were taken fromexperiments that were performed on a typical concrete tile floor and acarpet using “Rescue Randy”—a human mimic doll of weight 74 kg. and fourother common objects. Those experiments were performed at distances of 2to 5 meter from the sensors. Moreover, we drop objects and simulateevents that generate significant floor vibrations close to the sensorsin order to ensure the algorithm is effective in a multitude ofconditions. The drops of “Rescue Randy” are shown in FIGS. 13 a-b, andwere in a forward direction, based on statistics that 60% of falls thatoccur are in a forward direction. Typical of human falls, no two fallsof “Rescue Randy” are identical.

It will be appreciated that other human mimics, or even real falls, maybe used to provide training data for the system.

Training Phase Trials

In the training phase, “Rescue Randy” was dropped 10 times at eachdistance, for a total of 40 drop events. The objects that were droppedon the floor include a heavy bag (15 kg.), a book, a plastic box and ametal box. The objects were dropped 5 times at each distance, a total of20 times for each object, and thus a total of 80 drops of objects. Withincrease in distance from the sensors some of the non-human drops werenot detected at all, and in fact only 28 of the 80 drops of the objectswere detected as falling events. Therefore, only 28 events of fallingobjects in distances of 2-5 meters were included in the training set ofdata. The other trials that were performed close to the sensorsincluded: walking, dropping a chair, jumping from the chair on thefloor, dropping a heavy bag, dropping a plastic box, and dropping ametal box for a total of 12 events. Furthermore each trial was repeatedtwice. In total, the training set of data included 40 human falls and 40drops of objects and other events (28+12).

Testing Phase Trials

A second, testing, phase was carried out to see how well the trainingphase had worked. In the testing phase, trials consisted of 20 drops of“Rescue Randy” made up of 5 repetitions in each distance of 2-5 meters,and 48 drops of objects made up of 4 kinds of objects, each one with 3repetitions at each distance. Additional trials were performed close tothe sensors, in the same way as in the training phase, and theseincluded 3 repetitions of each event for a total of 18 events.

Furthermore, due to the fact that some apartments may be carpeted, wealso performed a testing set of experiments on a carpet. There were 5drops of “Rescue Randy” at distances of 2 to 5 meters on a carpet for atotal of 20 events. The testing set of data included a total of 40 dropsof “Rescue Randy” and 66 drops of objects and other events. In practicea system may be calibrated in situ based on the local floor type orfloor covering. An alternative is to provide a system with calibrationsfor all kinds of floors. The signals may be transformed based on thelocal type of floor to a uniform or normalized signal so that signalsfrom different kinds of floors can be classified by the same kind ofclassifier.

Results

A. Feature Selection:

The algorithm for feature selection ranked the performance of 110features that may be extracted from the training data. A set of 17 topperforming features was extracted for classification. Those featuresare: Lengths of vibration and sound event signals, Energy of vibrationevent signal, 11 SRS features and 3 MFCC features. Table 2 summarizesthe overall set of the selected features for classification.

TABLE 2 Features for classification Feature Selected Feature name symbolfeatures Vibration event L1 L1 length Sound event L2 L2 length Vibrationevent E1 E1 energy Sound event E2 — energy SRS S1-S93 S2, S10, S34, S64,S68, S74, S76, S77, S82, S84, S91 MFCC C1-C13 C3, C11, C12

During the process of the decision on the number of features forclassification, an aim was to obtain sensitivity and specificity of theclassification algorithm that are higher than 97%. Reference is now madeto FIG. 14 which plots sensitivity against number of features. As shownin FIG. 14, the required sensitivity and the specificity are achievedwhen the classification is performed by 17 features. Increasing thenumber of features for classification beyond 17, does not increase theperformance of the classification algorithm, and can cause over-fitting.Therefore, that number may be the optimal number of features forclassification.

B. Testing Phase Event Classification:

Table 3 summarizes the results of the detection and classificationalgorithm that was run on the testing database of the trials. Cases inwhich no event is detected may be classified as another event.

TABLE 3 Classification Results Real Events close “human” Objects to thesensors on a Class. As (“other event”) (“other event”) “human” carpet“other 44 (+4 undetected) 17 1 0 event” “human” 0 1 19 20

A Positive event is classified as a “human fall” event, and a negativeevent is classified as an “other event”. From the data presented, wefind the sensitivity of the system to be 97.5% and the specificity to be98.5%. The sensitivity of the system was calculated as the number of“Rescue Randy” drops that classified “human” (true positive) divided tothe total number of “Rescue Randy” drops (39/40). The specificity of thesystem was calculated as the number of objects and events close to thesensor that may be classified as “other event” (true negative) dividedby the total number of object drops and events close to the sensor(65/66). It should be mentioned that in the case of a false negative,“Rescue Randy” fell at a distance of 5 meter from the sensors. In thecases of false positive, the object fell close to the sensors generatingstrong signals that are similar to a human fall, and thereforeclassified as “human”.

General

The results of the laboratory tests of the present embodiment for falldetection show that the system may serve as a solution to the discussedproblem. Assuming that “Rescue Randy” is a good model for simulation ofhuman being fall events, the system can detect human falls with highprecision for distances up to 5 meters. The proposed solution is a lowcost solution, does not require the person to wear anything, and isconsiderate of privacy. The system is adaptive, can be calibrated to anykind of floor and room acoustics, and can be used not only in personalhomes but also in nursing homes.

In all the trials of “Rescue Randy” on the carpet, the events wereclassified as “human”. The conclusion of that result is that theclassification algorithm is strong enough for cases of human falldetection in which the classifier was not trained before. In cases ofobject drops in distances of 2 to 5 meter, in the training process 28out of 80 event were detected, and in the testing phase 44 out of 48event were detected. In total, 56.25% (=28+44/80+48) of the object dropswere detected. The system is thus demonstrated to be successful atdetection of human falls and effective at not falsely detecting otherevents. The difference between the percentage of detected object dropsin the training phase and the testing phases can be explained by higherSNR in the testing phase signals. Object drop events create lowamplitude vibration signals that sometimes cannot be distinguished fromthe surrounding noise. The change in the SNR can be explained byvariations in the system voltage on the days that the training andtesting phase experiments were performed. In cases of “Rescue Randy”drops in the training and testing phases, all the events were detected,and sent for classification. A conclusion is that the present system issensitive enough for the detection of human fall events. Highsensitivity of the event detection algorithm proves that the chosenaccelerometer was a good choice for measurement of floor vibrationsignals. Moreover, the classification algorithm may detect falls ofobjects at distances of 2 to 5 meter with a precision of substantially100%. This means that if the sensors of the proposed system are put inplaces in which there is a low chance of objects falling close to thesensors, the only positive events will be human falls. It is importantto note that the empirical threshold coefficient c₂ that was set to 0.1was good enough for the detection of all event-suspected windows in thetesting phase.

One limitation of the system is that it may not be sensitive to lowimpact real human falls in some cases that were not tested, e.g. slowand soft human falls onto the floor from a chair. However, the kind offalls that are capable of structural damage to bone, and in which theperson cannot get up, cause high amplitude floor vibrations that can bedetected by the system.

In deployment, the classification algorithm may be improved by trainingof the classification model, using various weights of “Rescue Randy”dolls in a wider variety of kinds of drops. Moreover, the algorithm maybe trained with drops of more objects on the floor and carpet, and withother events such as walking, door slams, environmental noise, and etc.that result in significant floor vibration and have sufficient energy totrigger the event detection algorithm. Tests that may further improvethe performance of the system include drops of “Rescue Randy” and theobjects at distances that are larger than 5 meters. Evaluation of thedistance in which the sensitivity is lower than 95% may help in planningthe location and number of the sensors, particularly in big rooms.Generally a deployment may require one accelerometer and microphone in asmall room (˜10 m2), and two such sensor units in a large room (˜25 m2).The sensors may be installed in one of the corners of the room.

For now, the event detection algorithm that is based on energycalculations is relatively simple. Specific in-situ testing may also beadvisable to ensure detection of ‘soft’ human falls and events in whichthe individual collides with another object, say a table, dresser, chairetc.

Ambient noise such as music and TV was found not to influence thedetection because the algorithm has to detect a vibration event tocorrespond to the sound. In spite of that, ambient noise couldconceivably influence the classification of the vibration event if say atelevision thump or scream happens to coincide with an actual butnon-fall related vibration event.

Training in the above tests was carried out on a typical concrete floor.Concrete transfers vibration signals with high attenuation because ithas sand under the tiles that absorbs the energy of the events. Wepreferred to choose that kind of floor because we wanted the algorithmto be able to detect and classify the events in real conditions. In reallife settings a large range of different constructions, floor covermaterials, and etc. exist. This may potentially have a significanteffect on the signals. Therefore, in the process of actual deploymentthere may be a process of calibration of the algorithms to the kind offloor.

As additional features, the fall detection system may be activated bydetection of a human call for help, thus by the detection of a person'sscream or crying, for example using a speech detection algorithm.

The contact with the emergency center in case of a detected fall eventmay be performed through the sensor units. In such a case these wouldinclude not only a microphone, but also a speaker.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims. All publications, patents, and patentapplications mentioned in this specification are herein incorporated intheir entirety by reference into the specification, to the same extentas if each individual publication, patent or patent application wasspecifically and individually indicated to be incorporated herein byreference. In addition, citation or identification of any reference inthis application shall not be construed as an admission that suchreference is available as prior art to the present invention.

What is claimed is:
 1. Apparatus for detection of human falls,comprising: an acceleration detector, for detecting vibration events,the acceleration detector attached to a floor, and configured to senselow amplitude vibrations with accelerations up to 2 g; a microphone, forplacing in association with said acceleration detector for detection ofsound events, and a classification unit comprising an acceleration inputfor input of data relating to said vibration events from saidacceleration detector and a sound input for input of data relating tosaid sound events from said microphone, the unit configured to classifyseparate events from said microphone and said acceleration detectorrespectively as concurrent events from said microphone and saidacceleration detector, thereby to determine whether a human fall isindicated, wherein said microphone has a frequency range ofsubstantially 20-16000 and an S/N ratio in excess of 58 dB.
 2. Theapparatus of claim 1, further comprising an alarm unit, associated withsaid classification unit for providing an alarm output when said humanfall is indicated.
 3. The apparatus of claim 1, wherein saidacceleration detector has a sensitivity to vibration of substantially1V/g.
 4. Apparatus for detection of human falls, comprising: anacceleration detector, for detecting vibration events, the accelerationdetector attached to a floor, and configured to sense low amplitudevibrations with accelerations up to 2 g; a microphone, for placing inassociation with said acceleration detector for detection of soundevents, and a classification unit comprising an acceleration input forinput of data relating to said vibration events from said accelerationdetector and a sound input for input of data relating to said soundevents from said microphone, the unit configured to classify separateevents from said microphone and said acceleration detector respectivelyas concurrent events from said microphone and said accelerationdetector, thereby to determine whether a human fall is indicatedwhereinsaid concurrent events comprise vibration events, and sound events, saidclassification unit being configured to extract features from saidevents, and to compare a distribution of said features with prestoredfeatures of learned events, thereby to classify said events as belongingto a human fall or not belonging to a human fall, and wherein saidextracted features comprise sound event energy and sound event lengthand Mel frequency cepstral coefficients, and vibration event energy, andvibration event length.
 5. The apparatus of claim 4, wherein saidclassification unit is configured to extract from said sound events asound event length feature and a sound event energy feature.
 6. Theapparatus of claim 4, wherein said classification unit is configured toextract from said vibration events a vibration event length and avibration event energy.
 7. The apparatus of claim 4, wherein saidextracted features comprise seventeen features.
 8. Method for detectionof human falls, comprising: detecting low amplitude vibration events ina floor with accelerations of up to 2 g, detecting sound events,classifying vibration and sound events detected at the same time asconcurrent vibration and sound events, and using said classifiedconcurrent events to determine whether a human fall is indicated whereinsaid classifying comprises extracting features from said events, andcomparing a distribution of said features with prestored features oflearned events, thereby to classify said events as belonging to a humanfall or not belonging to a human fall; and wherein said extractedfeatures comprise sound event energy and sound event length and at leastone member of the group consisting of shock response spectra, Melfrequency cepstral coefficients, vibration event energy, and vibrationevent length; the method further comprising detecting sound events usinga microphone, wherein said microphone has a frequency range ofsubstantially 20-16000 Hz and an S/N ratio in excess of 58 dB.
 9. Themethod of claim 8, further comprising providing an alarm output whensaid human fall is indicated.
 10. The method of claim 8, comprisingdetecting said vibration events using a sensitivity of substantially1V/g.
 11. The method of claim 8, wherein said classifying comprisesextracting from said sound events a sound event length feature and asound event energy feature.
 12. The method of claim 8, wherein saidclassifying comprises extracting from said vibration events a vibrationevent length and a vibration event energy.
 13. The method of claim 8,wherein said extracted features comprise shock response spectra, Melfrequency cepstral coefficients, vibration event energy, vibration eventlength, sound event energy and sound event length.
 14. The method ofclaim 11, wherein said extracted features comprise seventeen features.